no code implementations • 1 Apr 2024 • Jun Lyu, Chen Qin, Shuo Wang, Fanwen Wang, Yan Li, Zi Wang, Kunyuan Guo, Cheng Ouyang, Michael Tänzer, Meng Liu, Longyu Sun, Mengting Sun, Qin Li, Zhang Shi, Sha Hua, Hao Li, Zhensen Chen, Zhenlin Zhang, Bingyu Xin, Dimitris N. Metaxas, George Yiasemis, Jonas Teuwen, Liping Zhang, Weitian Chen, Yidong Zhao, Qian Tao, Yanwei Pang, Xiaohan Liu, Artem Razumov, Dmitry V. Dylov, Quan Dou, Kang Yan, Yuyang Xue, Yuning Du, Julia Dietlmeier, Carles Garcia-Cabrera, Ziad Al-Haj Hemidi, Nora Vogt, Ziqiang Xu, Yajing Zhang, Ying-Hua Chu, Weibo Chen, Wenjia Bai, Xiahai Zhuang, Jing Qin, Lianmin Wu, Guang Yang, Xiaobo Qu, He Wang, Chengyan Wang
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
no code implementations • 24 Feb 2024 • Zi Wang, Min Xiao, Yirong Zhou, Chengyan Wang, Naiming Wu, Yi Li, Yiwen Gong, Shufu Chang, Yinyin Chen, Liuhong Zhu, Jianjun Zhou, Congbo Cai, He Wang, Di Guo, Guang Yang, Xiaobo Qu
This challenge leads to necessitate extensive training data in many deep learning reconstruction methods.
no code implementations • 23 Jan 2024 • Jiayi Xie, Hongfeng Li, Jin Cheng, Qingrui Cai, Hanbo Tan, Lingyun Zu, Xiaobo Qu, Hongbin Han
Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Peclet number.
no code implementations • 21 Oct 2023 • Di Guo, Runmin Xu, Jinyu Wu, Meijin Lin, Xiaofeng Du, Xiaobo Qu
Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering.
no code implementations • 18 Oct 2023 • Yirong Zhou, Yanhuang Wu, Yuhan Su, Jing Li, Jianyun Cai, Yongfu You, Di Guo, Xiaobo Qu
The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format.
no code implementations • 21 Sep 2023 • Qingrui Cai, Liuhong Zhu, Jianjun Zhou, Chen Qian, Di Guo, Xiaobo Qu
PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data.
1 code implementation • 19 Sep 2023 • Chengyan Wang, Jun Lyu, Shuo Wang, Chen Qin, Kunyuan Guo, Xinyu Zhang, Xiaotong Yu, Yan Li, Fanwen Wang, Jianhua Jin, Zhang Shi, Ziqiang Xu, Yapeng Tian, Sha Hua, Zhensen Chen, Meng Liu, Mengting Sun, Xutong Kuang, Kang Wang, Haoran Wang, Hao Li, Yinghua Chu, Guang Yang, Wenjia Bai, Xiahai Zhuang, He Wang, Jing Qin, Xiaobo Qu
However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.
no code implementations • 13 Sep 2023 • Min Xiao, Zi Wang, Jiefeng Guo, Xiaobo Qu
Magnetic resonance imaging (MRI) plays an important role in modern medical diagnostic but suffers from prolonged scan time.
no code implementations • 12 Sep 2023 • Di Guo, Sijin Li, Jun Liu, Zhangren Tu, Tianyu Qiu, Jingjing Xu, Liubin Feng, Donghai Lin, Qing Hong, Meijin Lin, Yanqin Lin, Xiaobo Qu
Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation.
1 code implementation • 25 Jul 2023 • Zi Wang, Xiaotong Yu, Chengyan Wang, Weibo Chen, Jiazheng Wang, Ying-Hua Chu, Hongwei Sun, Rushuai Li, Peiyong Li, Fan Yang, Haiwei Han, Taishan Kang, Jianzhong Lin, Chen Yang, Shufu Chang, Zhang Shi, Sha Hua, Yan Li, Juan Hu, Liuhong Zhu, Jianjun Zhou, Meijing Lin, Jiefeng Guo, Congbo Cai, Zhong Chen, Di Guo, Guang Yang, Xiaobo Qu
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
no code implementations • 19 Jun 2023 • Xiaodie Chen, Jiayu Li, Dicheng Chen, Yirong Zhou, Zhangren Tu, Meijin Lin, Taishan Kang, Jianzhong Lin, Tao Gong, Liuhong Zhu, Jianjun Zhou, Lin Ou-yang, Jiefeng Guo, Jiyang Dong, Di Guo, Xiaobo Qu
We have shared our cloud platform at MRSHub, providing free access and service for two years.
no code implementations • 16 Jun 2023 • Dicheng Chen, Meijin Lin, Huiting Liu, Jiayu Li, Yirong Zhou, Taishan Kang, Liangjie Lin, Zhigang Wu, Jiazheng Wang, Jing Li, Jianzhong Lin, Xi Chen, Di Guo, Xiaobo Qu
Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.
no code implementations • 14 Apr 2023 • Chunyan Xiong, Mengli Lu, Xiaotong Yu, Jian Cao, Zhong Chen, Di Guo, Xiaobo Qu
Soft-thresholding has been widely used in neural networks.
no code implementations • 29 Dec 2022 • Jian Cao, Chen Qian, Yihui Huang, Dicheng Chen, Yuncheng Gao, Jiyang Dong, Di Guo, Xiaobo Qu
Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process.
no code implementations • 4 Dec 2022 • Yirong Zhou, Chen Qian, Jiayu Li, Zi Wang, Yu Hu, Biao Qu, Liuhong Zhu, Jianjun Zhou, Taishan Kang, Jianzhong Lin, Qing Hong, Jiyang Dong, Di Guo, Xiaobo Qu
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
no code implementations • 2 Dec 2022 • Tianyu Qiu, Amir Jahangiri, Xiao Han, Dmitry Lesovoy, Tatiana Agback, Peter Agback, Adnane Achour, Xiaobo Qu, Vladislav Orekhov
Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine.
no code implementations • 24 Nov 2022 • Yihui Huang, Zi Wang, Xinlin Zhang, Jian Cao, Zhangren Tu, Meijin Lin, Di Guo, Xiaobo Qu
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts.
no code implementations • 23 Oct 2022 • Zi Wang, Haoming Fang, Chen Qian, Boxuan Shi, Lijun Bao, Liuhong Zhu, Jianjun Zhou, Wenping Wei, Jianzhong Lin, Di Guo, Xiaobo Qu
To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results.
no code implementations • 20 Oct 2022 • Chen Qian, Yuncheng Gao, Mingyang Han, Zi Wang, Dan Ruan, Yu Shen, Yaping Wu, Yirong Zhou, Chengyan Wang, Boyu Jiang, Ran Tao, Zhigang Wu, Jiazheng Wang, Liuhong Zhu, Yi Guo, Taishan Kang, Jianzhong Lin, Tao Gong, Chen Yang, Guoqiang Fei, Meijin Lin, Di Guo, Jianjun Zhou, Meiyun Wang, Xiaobo Qu
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
1 code implementation • 30 Jul 2022 • Fanyou Wu, Yang Liu, Rado Gazo, Benes Bedrich, Xiaobo Qu
In the Amazon KDD Cup 2022, we aim to apply natural language processing methods to improve the quality of search results that can significantly enhance user experience and engagement with search engines for e-commerce.
no code implementations • 28 Mar 2022 • Chen Qian, Zi Wang, Xinlin Zhang, Boxuan Shi, Boyu Jiang, Ran Tao, Jing Li, Yuwei Ge, Taishan Kang, Jianzhong Lin, Di Guo, Xiaobo Qu
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
no code implementations • 21 Mar 2022 • Qinqin Yang, Zi Wang, Kunyuan Guo, Congbo Cai, Xiaobo Qu
Deep learning has innovated the field of computational imaging.
no code implementations • 18 Feb 2022 • Jie Zhu, Ivana Tasic, Xiaobo Qu
The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions.
no code implementations • 9 Dec 2021 • Zi Wang, Chen Qian, Di Guo, Hongwei Sun, Rushuai Li, Bo Zhao, Xiaobo Qu
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI).
no code implementations • 4 Aug 2021 • Jie Zhu, Ivana Tasic, Xiaobo Qu
Freeway on-ramps are typical bottlenecks in the freeway network due to the frequent disturbances caused by their associated merging, weaving, and lane-changing behaviors.
no code implementations • 24 Jul 2021 • Xinlin Zhang, Hengfa Lu, Di Guo, Zongying Lai, Huihui Ye, Xi Peng, Bo Zhao, Xiaobo Qu
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time.
no code implementations • 18 Apr 2021 • Yirong Zhou, Chen Qian, Yi Guo, Zi Wang, Jian Wang, Biao Qu, Di Guo, Yongfu You, Xiaobo Qu
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
no code implementations • 26 Jan 2021 • Dicheng Chen, Wanqi Hu, Huiting Liu, Yirong Zhou, Tianyu Qiu, Yihui Huang, Zi Wang, Jiazheng Wang, Liangjie Lin, Zhigang Wu, Hao Chen, Xi Chen, Gen Yan, Di Guo, Jianzhong Lin, Xiaobo Qu
A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).
1 code implementation • 29 Dec 2020 • Zi Wang, Di Guo, Zhangren Tu, Yihui Huang, Yirong Zhou, Jian Wang, Liubin Feng, Donghai Lin, Yongfu You, Tatiana Agback, Vladislav Orekhov, Xiaobo Qu
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.
no code implementations • 13 Nov 2020 • Fanyou Wu, Yang Liu, Zhiyuan Liu, Xiaobo Qu, Rado Gazo, Eva Haviarova
In our 2020 Competition solution, we further design multiple variants based on HR-NET and UNet.
no code implementations • 10 Sep 2020 • Yongzhi Zhang, Xiaobo Qu, Lang Tong
In this real time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge.
no code implementations • 13 Jul 2020 • Yihui Huang, Jinkui Zhao, Zi Wang, Vladislav Orekhov, Di Guo, Xiaobo Qu
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing.
no code implementations • 13 Jan 2020 • Dicheng Chen, Zi Wang, Di Guo, Vladislav Orekhov, Xiaobo Qu
In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
no code implementations • 24 Sep 2019 • Tieyuan Lu, Xinlin Zhang, Yihui Huang, Yonggui Yang, Gang Guo, Lijun Bao, Feng Huang, Di Guo, Xiaobo Qu
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time.
no code implementations • 17 Sep 2019 • Xinlin Zhang, Hengfa Lu, Di Guo, Lijun Bao, Feng Huang, Qin Xu, Xiaobo Qu
The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging.
no code implementations • 9 Apr 2019 • Xiaobo Qu, Yihui Huang, Hengfa Lu, Tianyu Qiu, Di Guo, Tatiana Agback, Vladislav Orekhov, Zhong Chen
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.
no code implementations • 6 Apr 2016 • Jiaxi Ying, Hengfa Lu, Qingtao Wei, Jian-Feng Cai, Di Guo, Jihui Wu, Zhong Chen, Xiaobo Qu
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.
no code implementations • 29 Apr 2015 • Yunsong Liu, Zhifang Zhan, Jian-Feng Cai, Di Guo, Zhong Chen, Xiaobo Qu
It has been shown that, redundant image representations, e. g. tight frames, can significantly improve the image quality.
no code implementations • 10 Mar 2015 • Zhifang Zhan, Jian-Feng Cai, Di Guo, Yunsong Liu, Zhong Chen, Xiaobo Qu
The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods.
no code implementations • 10 Mar 2015 • Jian-Feng Cai, Xiaobo Qu, Weiyu Xu, Gui-Bo Ye
Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of $R$ complex sinusoids.